Electrochemical Model-Based State of Charge and State of Health Estimation of Lithium-Ion Batteries

Vehicle electrification continues to be a key topic of interest for automotive manufacturers, in an effort to reduce the usage of fossil fuel energy and improve vehicle efficiency. Lithium-ion batteries are currently the technology of choice for hybrid and electric vehicles due to their decreasing cost and improved power and energy density over traditional lead-acid or nickel-metal-hydride batteries. In particular, batteries with composite electrodes have seen increased use in automotive applications due to their ability to balance energy density, power density, and cost by adjusting the amount of each material within the electrode. However, this improved performance introduces new challenges to ensure the battery pack operates safely, reliably, and durably. The vehicle's battery management system (BMS) is designed to meet these challenges, in part, by estimating the battery state of charge (SOC) and state of health (SOH). Knowledge of SOC allows the BMS to predict the available instantaneous power while ensuring the battery is operating within safe limits. As batteries age, they lose capacity and the ability to deliver power. Therefore, tracking the battery SOH is necessary to maintain accurate estimates of SOC and power throughout the battery life and give an accurate miles-to-empty metric to the driver.;Recently, increased attention has been given to electrochemical models for SOC and SOH estimation, over traditional circuit models. Electrochemical models based on first principles have the potential to more accurately predict cell performance, as well as provide more information about the internal battery states. State of health estimation algorithms that do not use electrochemical-based models may have more difficulty maintaining an accurate battery model as the cell ages under varying degradation modes such as lithium consumption at the solid-electrolyte interface or active material dissolution. However, efforts to validate electrochemical model-based state estimation algorithms with experimental aging data are limited, particularly for composite electrode cells.;This dissertation focuses on applying electrochemical models towards SOC and SOH estimation problems, specifically on automotive cells with a composite LiMn2O4-LiNi1/3Mn1/3 Co1/3O2 (LMO-NMC) cathode. Model order reduction techniques are applied to reduce the computational complexity of the governing electrochemical equations, while still maintaining physically meaningful parameters. The observability of the reduced order model is analyzed in order to facilitate state estimation, while also determining the limitations on the number of capacity and power related parameters that can be estimated simultaneously. Various nonlinear observers are implemented to estimate the model states associated with SOC and SOH, in order to determine their applicability in an onboard battery management system. Finally, SOC and SOH estimates are validated with experimental aging data.